Add new text document to a Vector Store

POST /vector-stores/{vector-store-id}/documents/create

Create a new text document in a vector store.

Parameter	In	Type	Required	Description
vector-store-id	path	string	yes	The ID of the vector store.
x-api-key	header	string	yes	The API key for authentication.

Request body

Field	Type	Required	Description
name	string		The name of the document.
text	string		The text content of the document.

Responses

• 201 — Document created successfully.

Example Requests

cURLJavaScriptPython

curl -X POST   
  https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/create   
  -H "x-api-key: YOUR_API_KEY"   
  -H "Content-Type: application/json"   
  -d '{
    "name": "Product Overview",
    "text": "Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities."
  }'

Info

Replace YOUR_API_KEY with your actual Vector Store API key and vs_abc123 with your Vector Store ID.

const addTextDocument = async (vectorStoreId, name, text) => {
  const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/create`, {
    method: 'POST',
    headers: {
      'x-api-key': 'YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      name: name,
      text: text
    })
  });

  const data = await response.json();
  console.log(data);
};

addTextDocument(
  'vs_abc123',
  'Product Overview',
  'Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities.'
);

Info

Replace YOUR_API_KEY with your actual Vector Store API key and vs_abc123 with your Vector Store ID.

import requests
import json

def add_text_document(vector_store_id, name, text):
    headers = {
        "x-api-key": "YOUR_API_KEY",
        "Content-Type": "application/json"
    }

    payload = {
        "name": name,
        "text": text
    }

    response = requests.post(
        f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/create",
        headers=headers,
        data=json.dumps(payload)
    )

    data = response.json()
    print(data)

add_text_document(
    'vs_abc123',
    'Product Overview',
    'Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities.'
)

Info

Replace YOUR_API_KEY with your actual Vector Store API key and vs_abc123 with your Vector Store ID.

Path Parameters

Parameter	Type	Description
vector-store-id	string	Required. The ID of the vector store to add the document to.

Request Body

{
  "name": "Product Overview",
  "text": "Our product is an innovative solution for managing vector embeddings. It provides seamless integration with your existing systems and offers powerful semantic search capabilities."
}

Parameter	Type	Description
name	string	Required. The name of the document.
text	string	Required. The text content of the document.

Response Format

{
  "id": 1234,
  "name": "Product Overview",
  "type": "text/plain",
  "vector_store": "vs_abc123",
  "size": 173,
  "status": "processing",
  "processing_status": "queued",
  "indexed_on": null,
  "status_error_message": null,
  "created": "2023-06-15T10:15:00Z",
  "modified": "2023-06-15T10:15:00Z"
}

Authentication

This endpoint requires authentication using an API key in the x-api-key header.

Error Responses

Status Code	Description
400	Bad Request - Missing required fields or invalid format
401	Unauthorized - Invalid or missing API key
404	Not Found - Vector Store not found
500	Internal Server Error

This endpoint allows you to add text content directly to your vector store. It's particularly useful for storing information that might not exist in file format, such as product descriptions, knowledge base articles, or custom content. The text will be automatically processed into vector embeddings, making it searchable using semantic similarity.